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<!-- ==================== CLASS DESCRIPTION ==================== -->
<h1 class="epydoc">Class GoldenRule</h1><p class="nomargin-top"><span class="codelink"><a href="peach.optm.linear-pysrc.html#GoldenRule">source&nbsp;code</a></span></p>
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<p>Optimizer by the Golden Section Rule</p>
<p>This optimizer uses the golden rule to section an interval in search of the
minimum. Using a simple heuristic, the interval is refined until an interval
small enough to satisfy the error requirements is found.</p>

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          <td><span class="summary-sig"><a href="peach.optm.linear.GoldenRule-class.html#__init__" class="summary-sig-name">__init__</a>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">f</span>,
        <span class="summary-sig-arg">x0</span>,
        <span class="summary-sig-arg">emax</span>=<span class="summary-sig-default">1e-08</span>,
        <span class="summary-sig-arg">imax</span>=<span class="summary-sig-default">1000</span>)</span><br />
      Initializes the optimizer.</td>
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          <td><span class="summary-sig"><a name="__set_x"></a><span class="summary-sig-name">__set_x</span>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">x0</span>)</span></td>
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            <span class="codelink"><a href="peach.optm.linear-pysrc.html#GoldenRule.__set_x">source&nbsp;code</a></span>
            
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          <td><span class="summary-sig"><a href="peach.optm.linear.GoldenRule-class.html#restart" class="summary-sig-name">restart</a>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">x0</span>)</span><br />
      Resets the optimizer, returning to its original state, and allowing to
use a new first estimate.</td>
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            <span class="codelink"><a href="peach.optm.linear-pysrc.html#GoldenRule.restart">source&nbsp;code</a></span>
            
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          <td><span class="summary-sig"><a href="peach.optm.linear.GoldenRule-class.html#step" class="summary-sig-name">step</a>(<span class="summary-sig-arg">self</span>)</span><br />
      One step of the search.</td>
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            <span class="codelink"><a href="peach.optm.linear-pysrc.html#GoldenRule.step">source&nbsp;code</a></span>
            
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          <td><span class="summary-sig"><a href="peach.optm.linear.GoldenRule-class.html#__call__" class="summary-sig-name">__call__</a>(<span class="summary-sig-arg">self</span>)</span><br />
      Transparently executes the search until the minimum is found. The stop
criteria are the maximum error or the maximum number of iterations,
whichever is reached first. Note that this is a <tt class="rst-docutils literal">__call__</tt> method, so
the object is called as a function. This method returns a tuple
<tt class="rst-docutils literal">(x, e)</tt>, with the best estimate of the minimum and the error.</td>
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            <span class="codelink"><a href="peach.optm.linear-pysrc.html#GoldenRule.__call__">source&nbsp;code</a></span>
            
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    <p class="indent-wrapped-lines"><b>Inherited from <code>object</code></b>:
      <code>__delattr__</code>,
      <code>__format__</code>,
      <code>__getattribute__</code>,
      <code>__hash__</code>,
      <code>__new__</code>,
      <code>__reduce__</code>,
      <code>__reduce_ex__</code>,
      <code>__repr__</code>,
      <code>__setattr__</code>,
      <code>__sizeof__</code>,
      <code>__str__</code>,
      <code>__subclasshook__</code>
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<!-- ==================== PROPERTIES ==================== -->
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      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
        <a href="peach.optm.linear.GoldenRule-class.html#x" class="summary-name">x</a><br />
      The estimate of the position of the minimum.
    </td>
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    <p class="indent-wrapped-lines"><b>Inherited from <code>object</code></b>:
      <code>__class__</code>
      </p>
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<a name="__init__"></a>
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  <h3 class="epydoc"><span class="sig"><span class="sig-name">__init__</span>(<span class="sig-arg">self</span>,
        <span class="sig-arg">f</span>,
        <span class="sig-arg">x0</span>,
        <span class="sig-arg">emax</span>=<span class="sig-default">1e-08</span>,
        <span class="sig-arg">imax</span>=<span class="sig-default">1000</span>)</span>
    <br /><em class="fname">(Constructor)</em>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="peach.optm.linear-pysrc.html#GoldenRule.__init__">source&nbsp;code</a></span>&nbsp;
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  <p>Initializes the optimizer.</p>
<p>To create an optimizer of this type, instantiate the class with the
parameters given below:</p>
  <dl class="fields">
    <dt>Parameters:</dt>
    <dd><ul class="nomargin-top">
        <li><strong class="pname"><code>f</code></strong> - A one variable only function to be optimized. The function should
have only one parameter and return the function value.</li>
        <li><p><strong class="pname"><code>x0</code></strong> - First estimate of the minimum. The golden rule search needs two
estimates to partition the interval. Thus, the first estimate must
be a duple <tt class="rst-docutils literal">(xl, xh)</tt>, with the property that <tt class="rst-docutils literal">xl &lt; xh</tt>. Be
aware, however, that no checking is done -- if the estimate doesn't
correspond to this condition, in some point an exception will be
raised.</p>
<p>Notice that, given the nature of the estimate of the golden rule
method, it is not necessary to have a specific parameter to restrict
the range of acceptable values -- it is already embedded in the
estimate. If you need to restrict your estimate between an interval,
just use its limits as <tt class="rst-docutils literal">xl</tt> and <tt class="rst-docutils literal">xh</tt> in the estimate.</p></li>
        <li><strong class="pname"><code>emax</code></strong> - Maximum allowed error. The algorithm stops as soon as the error is
below this level. The error is absolute.</li>
        <li><strong class="pname"><code>imax</code></strong> - Maximum number of iterations, the algorithm stops as soon this
number of iterations are executed, no matter what the error is at
the moment.</li>
    </ul></dd>
    <dt>Overrides:
        object.__init__
    </dt>
  </dl>
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  <h3 class="epydoc"><span class="sig"><span class="sig-name">restart</span>(<span class="sig-arg">self</span>,
        <span class="sig-arg">x0</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="peach.optm.linear-pysrc.html#GoldenRule.restart">source&nbsp;code</a></span>&nbsp;
    </td>
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  Resets the optimizer, returning to its original state, and allowing to
use a new first estimate.
  <dl class="fields">
    <dt>Parameters:</dt>
    <dd><ul class="nomargin-top">
        <li><strong class="pname"><code>x0</code></strong> - The new value of the estimate of the minimum. The golden rule search
needs two estimates to partition the interval. Thus, the estimate
must be a duple <tt class="rst-docutils literal">(xl, xh)</tt>, with the property that <tt class="rst-docutils literal">xl &lt; xh</tt>.</li>
    </ul></dd>
  </dl>
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  <h3 class="epydoc"><span class="sig"><span class="sig-name">step</span>(<span class="sig-arg">self</span>)</span>
  </h3>
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    ><span class="codelink"><a href="peach.optm.linear-pysrc.html#GoldenRule.step">source&nbsp;code</a></span>&nbsp;
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  <p>One step of the search.</p>
<p>In this method, the result of the step is dependent only of the given
estimated, so it can be used for different kind of investigations on the
same cost function.</p>
  <dl class="fields">
    <dt>Returns:</dt>
        <dd>This method returns a tuple <tt class="rst-docutils literal">(x, e)</tt>, where <tt class="rst-docutils literal">x</tt> is the updated
duple of estimates of the minimum, and <tt class="rst-docutils literal">e</tt> is the estimated error.</dd>
    <dt>Overrides:
        <a href="peach.optm.base.Optimizer-class.html#step">base.Optimizer.step</a>
    </dt>
  </dl>
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<a name="__call__"></a>
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  <h3 class="epydoc"><span class="sig"><span class="sig-name">__call__</span>(<span class="sig-arg">self</span>)</span>
    <br /><em class="fname">(Call operator)</em>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="peach.optm.linear-pysrc.html#GoldenRule.__call__">source&nbsp;code</a></span>&nbsp;
    </td>
  </tr></table>
  
  Transparently executes the search until the minimum is found. The stop
criteria are the maximum error or the maximum number of iterations,
whichever is reached first. Note that this is a <tt class="rst-rst-docutils literal rst-docutils literal">__call__</tt> method, so
the object is called as a function. This method returns a tuple
<tt class="rst-rst-docutils literal rst-docutils literal">(x, e)</tt>, with the best estimate of the minimum and the error.
  <dl class="fields">
    <dt>Returns:</dt>
        <dd>This method returns a tuple <tt class="rst-docutils literal">(x, e)</tt>, where <tt class="rst-docutils literal">x</tt> is the best
estimate of the minimum, and <tt class="rst-docutils literal">e</tt> is the estimated error.</dd>
    <dt>Overrides:
        <a href="peach.optm.base.Optimizer-class.html#__call__">base.Optimizer.__call__</a>
    </dt>
  </dl>
</td></tr></table>
</div>
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  <h3 class="epydoc">x</h3>
  The estimate of the position of the minimum.
  <dl class="fields">
    <dt>Get Method:</dt>
    <dd class="value"><span class="summary-sig"><a href="peach.optm.linear.GoldenRule-class.html#__get_x" class="summary-sig-name" onclick="show_private();">__get_x</a>(<span class="summary-sig-arg">self</span>)</span>
    </dd>
    <dt>Set Method:</dt>
    <dd class="value"><span class="summary-sig"><a href="peach.optm.linear.GoldenRule-class.html#__set_x" class="summary-sig-name" onclick="show_private();">__set_x</a>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">x0</span>)</span>
    </dd>
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